Examining the Variability of Sleep Patterns during Treatment for Chronic Insomnia: Application of a Location-Scale Mixed Model

Can mindfulness-based interventions improve outcomes in cognitive-behavioural therapy for chronic insomnia disorder in the general population? Systematic review and meta-analysis

Cognitive-behavioural therapy for insomnia (CBT-I) is the recommended first-line therapy for adults with chronic insomnia disorder (ID), which is characterized by hyperarousal. Mindfulness-based interventions (MBIs) are protocols aimed at stress reduction based on non-judgmental attention control in the present moment. However, MBIs have been increasingly used without a clear scientific basis. The objective of this analysis was to examine if MBIs could be useful as a component of the CBT-I therapeutic system through a systematic review and meta-analysis of randomized controlled trials (RCTs) and non-randomized studies (NRS) searched in PubMed, PsycINFO, Cochrane and WoS. The Insomnia Severity Index (ISI) was the primary outcome, while the Pittsburgh Sleep Quality Index (PSQI) and a composite sleep variable (CSV) were secondary outcomes. Thirteen articles corresponding to nine studies (three pragmatic RCTs, three explanatory RCTs and three NRS) were included. The omnibus test found that MBIs had a small to medium effect size on ISI nearing signification when comparing active control groups in the pretest-posttest period [Δ = 0.44, p = 0.07], a medium, non-significant, effect size on PSQI [Δ = 0.52, p = 0.18], and a significant though small effect size on CSV [Δ = 0.05, p < 0.01]. No heterogeneity was found. The analysis could not demonstrate that MBIs, combined with CBT-I components in some studies, positively affected ID in the general adult population. This was probably due to the lack of pragmatic designs and suitable measuring instruments. Recommendations are made for designing further studies to address these issues.

Measuring sleep regularity: Theoretical properties and practical usage of existing metrics

STUDY OBJECTIVES

Sleep regularity predicts many health-related outcomes. Currently, however, there is no systematic approach to measuring sleep regularity. Traditionally, metrics have assessed deviations in sleep patterns from an individual's average. Traditional metrics include intra-individual standard deviation (StDev), Interdaily Stability (IS), and Social Jet Lag (SJL). Two metrics were recently proposed that instead measure variability between consecutive days: Composite Phase Deviation (CPD) and Sleep Regularity Index (SRI). Using large-scale simulations, we investigated the theoretical properties of these five metrics.

METHODS

Multiple sleep-wake patterns were systematically simulated, including variability in daily sleep timing and/or duration. Average estimates and 95% confidence intervals were calculated for six scenarios that affect measurement of sleep regularity: 'scrambling' the order of days; daily vs. weekly variation; naps; awakenings; 'all-nighters'; and length of study.

RESULTS

SJL measured weekly but not daily changes. Scrambling did not affect StDev or IS, but did affect CPD and SRI; these metrics, therefore, measure sleep regularity on multi-day and day-to-day timescales, respectively. StDev and CPD did not capture sleep fragmentation. IS and SRI behaved similarly in response to naps and awakenings but differed markedly for all-nighters. StDev and IS required over a week of sleep-wake data for unbiased estimates, whereas CPD and SRI required larger sample sizes to detect group differences.

CONCLUSIONS

Deciding which sleep regularity metric is most appropriate for a given study depends on a combination of the type of data gathered, the study length and sample size, and which aspects of sleep regularity are most pertinent to the research question.

Family contexts and sleep during adolescence

Sleep deprivation among adolescents has received much attention from health researchers and policymakers. Recent research indicates that variation in sleep duration from night to night is associated with multiple health outcomes. While there is evidence that sleep deprivation is socially patterned, we know little about how social contexts are associated with nightly sleep variation during adolescence (a life course stage when nightly sleep variation is particularly high). Given the importance of family environments for influencing adolescents’ sleep patterns, we hypothesized that disadvantaged family contexts would be associated with higher intra-individual variation (IIV) in nightly sleep duration, in addition to lower average nightly sleep duration. We tested these hypotheses in a diverse, population-based sample of 11–17 year-olds (N = 1095) from the Adolescent Health and Development in Context Study. Using survey and ecological momentary assessment data and a novel form of multi-level regression modeling (location-scale mixed modeling), we found that adolescents living in unmarried-parent, low SES, economically insecure, and high caregiver stress families had higher IIV in sleep than adolescents in families with more resources and less caregiver stress. There were fewer family effects on average sleep duration. This suggests family social and economic contexts are associated with an under-researched aspect of adolescent sleep, nightly variation, and may contribute to adolescent sleep problems with implications for their health and health disparities.

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Consistency in nightly sleep duration is important for adolescent health.

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SES and family stress associated with variability in sleep duration in adolescents.

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Caregiver distress also associated with lower mean sleep duration among adolescents.

The Power to Detect and Predict Individual Differences in Intra-Individual Variability Using the Mixed-Effects Location-Scale Model

Our goal is to provide empirical scientists with practical tools and advice with which to test hypotheses related to individual differences in intra-individual variability using the mixed-effects location-scale model. To that end, we evaluate Type I error rates and power to detect and predict individual differences in intra-individual variability using this model and provide empirically-based guidelines for building scale models that include random and/or systematically-varying fixed effects. We also provide two power simulation programs that allow researchers to conduct a priori empirical power analyses. Our results aligned with statistical power theory, in that, greater power was observed for designs with more individuals, more repeated occasions, greater proportions of variance available to be explained, and larger effect sizes. In addition, our results indicated that Type I error rates were acceptable in situations when individual differences in intra-individual variability were not initially detectable as well as when the scale-model individual-level predictor explained all initially detectable individual differences in intra-individual variability. We conclude our paper by providing study design and model building advice for those interested in using the mixed-effects location-scale model in practice.

Dismantling Mindfulness-Based Cognitive Therapy: Creation and validation of 8-week focused attention and open monitoring interventions within a 3-armed randomized controlled trial

BACKGROUND

While mindfulness-based interventions (MBIs) employ two distinct practices, focused attention (FA) and open monitoring (OM), the integrated delivery of these practices in MBIs precludes understanding of their practice-specific effects or mechanisms of action. The purpose of this study is to isolate hypothesized active ingredients and practice-specific mechanistic target engagement by creating structurally equivalent interventions that differ only by the active ingredient (meditation practice) offered and to test whether the hypothesized components differentially engage the mechanistic targets that they are purported to engage.

METHODS

Participants were intended to be representative of American meditators and had mild to severe affective disturbances. Measures of structural equivalence included participant-level (sample characteristics), treatment-level (program structure and duration, program materials, class size, attendance, homework compliance, etc.), and instructor-level variables (training, ratings and adherence/fidelity). Measures of differential validity included analysis of program materials and verification of differential mechanistic target engagement (cognitive and affective skills and beliefs about meditation acquired by participants after the 8-week training).

RESULTS

The results indicate successful creation of structurally equivalent FA and OM programs that were matched on participant-level, treatment-level, and instructor-level variables. The interventions also differed as expected with respect to program materials as well as mechanistic targets engaged (skills and beliefs acquired).

CONCLUSIONS

These validated 8-week FA and OM training programs can be applied in future research to assess practice-specific effects of meditation.

Using Mindfulness for the Treatment of Insomnia

Purpose of review

The goal of this review is to provide an update on the use of mindfulness meditation for the treatment of insomnia, including conceptual models and empirical evidence from randomized controlled trials.

Recent findings

A metacognitive model of insomnia has been proposed as a conceptual model to explain the application of mindfulness principles for reducing insomnia-related arousal. Furthermore, the evidence base for mindfulness-based therapies has grown with the results of several randomized controlled trials published in the past three years. Treatment effects appear to be strongest on self-report measures compared to objective measures of sleep.

Summary

Treatment programs featuring mindfulness meditation appear to be viable treatment options for people with insomnia. Further research is needed to determine who is likely to benefit from mindfulness-based therapies and how these interventions work. Additionally, further work is needed to resolve issues related to the delivery and implementation of mindfulness-based therapies.